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Description

This research presents a large scale system for detection of visual
concepts and annotation of images. The system is composed of two parts:
feature extraction and classification/ annotation. The feature
extraction part provides global and local descriptions of the images in
the form of numerical vectors. Using these numerical descriptions, we
train a classifier, a predictive clustering tree (PCT), to produce
annotations for unseen images. PCTs are able to handle target concepts
that are organized in a hierarchy, i.e., perform hierarchical
multi-label classification. To improve the classification performance,
we construct ensembles (bags and random forests) of PCTs.

We evaluate our system on two different databases: IRMA database which
contains medical images and the image database from the ImageCLEF@ICPR
2010 photo annotation task which contains general images. The extensive
experiments conducted on the benchmark databases show that our system
has very high predictive performance and can be easily scaled to large
amounts of visual concepts and data. In addition, our approach is very
general: it can be easily extended with new feature extraction methods,
and it can thus be easily applied to other domains, types of images and
other classification schemes. Furthermore, it can handle arbitrarily
sized hierarchies organized as trees or directed acyclic graphs.